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05/02/2023
Unlocking the Power of Representations in Long-term Novelty-based Exploration
We introduce Robust Exploration via Clustering-based Online Density Esti...
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03/14/2023
Fast Rates for Maximum Entropy Exploration
We consider the reinforcement learning (RL) setting, in which the agent ...
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12/06/2022
Understanding Self-Predictive Learning for Reinforcement Learning
We study the learning dynamics of self-predictive learning for reinforce...
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09/28/2022
Optimistic Posterior Sampling for Reinforcement Learning with Few Samples and Tight Guarantees
We consider reinforcement learning in an environment modeled by an episo...
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06/16/2022
BYOL-Explore: Exploration by Bootstrapped Prediction
We present BYOL-Explore, a conceptually simple yet general approach for ...
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04/10/2022
Information-theoretic Online Memory Selection for Continual Learning
A challenging problem in task-free continual learning is the online sele...
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01/30/2022
Scaling Gaussian Process Optimization by Evaluating a Few Unique Candidates Multiple Times
Computing a Gaussian process (GP) posterior has a computational cost cub...
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11/03/2021
One Pass ImageNet
We present the One Pass ImageNet (OPIN) problem, which aims to study the...
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06/23/2021
ParK: Sound and Efficient Kernel Ridge Regression by Feature Space Partitions
We introduce ParK, a new large-scale solver for kernel ridge regression....
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04/29/2021
On the Emergence of Whole-body Strategies from Humanoid Robot Push-recovery Learning
Balancing and push-recovery are essential capabilities enabling humanoid...
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06/30/2020
Sampling from a k-DPP without looking at all items
Determinantal point processes (DPPs) are a useful probabilistic model fo...
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02/23/2020
Near-linear Time Gaussian Process Optimization with Adaptive Batching and Resparsification
Gaussian processes (GP) are one of the most successful frameworks to mod...
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08/27/2019
Statistical and Computational Trade-Offs in Kernel K-Means
We investigate the efficiency of k-means in terms of both statistical an...
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05/31/2019
Exact sampling of determinantal point processes with sublinear time preprocessing
We study the complexity of sampling from a distribution over all index s...
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03/13/2019
Gaussian Process Optimization with Adaptive Sketching: Scalable and No Regret
Gaussian processes (GP) are a popular Bayesian approach for the optimiza...
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10/31/2018
On Fast Leverage Score Sampling and Optimal Learning
Leverage score sampling provides an appealing way to perform approximate...
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03/27/2018
Distributed Adaptive Sampling for Kernel Matrix Approximation
Most kernel-based methods, such as kernel or Gaussian process regression...
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06/15/2017
Second-Order Kernel Online Convex Optimization with Adaptive Sketching
Kernel online convex optimization (KOCO) is a framework combining the ex...
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09/13/2016
Analysis of Kelner and Levin graph sparsification algorithm for a streaming setting
We derive a new proof to show that the incremental resparsification algo...
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01/21/2016
Incremental Spectral Sparsification for Large-Scale Graph-Based Semi-Supervised Learning
While the harmonic function solution performs well in many semi-supervis...
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04/30/2013